Lee Chien-Cheng, Chung Pau-Choo, Tsai Hong-Ming
Department of Electrical Engineering, National Cheng-Kung University, Tainan, 70101 Taiwan, ROC.
IEEE Trans Inf Technol Biomed. 2003 Sep;7(3):208-17. doi: 10.1109/titb.2003.813795.
Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist in teaching, clinical training, diagnosis, and medical image retrieval. However, due to partial volume effects, gray-level similarities of adjacent organs, contrast media affect, and the relatively high variations of organ position and shape, automatically identifying abdominal organs has always been a high challenging task. To conquer these difficulties, this paper proposes combining a multimodule contextual neural network and spatial fuzzy rules and fuzzy descriptors for automatically identifying abdominal organs from a series of CT image slices. The multimodule contextual neural network segments each image slice through a divide-and-conquer concept, embedded within multiple neural network modules, where the results obtained from each module are forwarded to other modules for integration, in which contextual constraints are enforced. With this approach, the difficulties arising from partial volume effects, gray-level similarities of adjacent organs, and contrast media affect can be reduced to the extreme. To address the issue of high variations in organ position and shape, spatial fuzzy rules and fuzzy descriptors are adopted, along with a contour modification scheme implementing consecutive organ region overlap constraints. This approach has been tested on 40 sets of abdominal CT images, where each set consists of about 40 image slices. We have found that 99% of the organ regions in the test images are correctly identified as its belonging organs, implying the high promise of the proposed method.
识别腹部器官是可视化器官结构以辅助教学、临床培训、诊断和医学图像检索的关键步骤之一。然而，由于部分容积效应、相邻器官的灰度相似性、造影剂影响以及器官位置和形状的相对高度变化，自动识别腹部器官一直是一项极具挑战性的任务。为克服这些困难，本文提出将多模块上下文神经网络与空间模糊规则及模糊描述符相结合，用于从一系列CT图像切片中自动识别腹部器官。多模块上下文神经网络通过分治概念对每个图像切片进行分割，该概念嵌入多个神经网络模块中，其中每个模块获得的结果会转发到其他模块进行整合，并在其中施加上下文约束。通过这种方法，由部分容积效应、相邻器官的灰度相似性和造影剂影响引起的困难可以被最大限度地减少。为解决器官位置和形状的高度变化问题，采用了空间模糊规则和模糊描述符，以及一种实施连续器官区域重叠约束的轮廓修正方案。该方法已在40组腹部CT图像上进行了测试，每组图像约由40个图像切片组成。我们发现，测试图像中99%的器官区域被正确识别为其所属器官，这意味着所提出的方法具有很高的前景。
